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Intracellular lipid droplets are associated with a myriad of afflictions including obesity, fatty liver disease, coronary artery disease, and infectious diseases (eg, HCV and tuberculosis). To develop high-content analysis (HCA) techniques to analyze lipid droplets and associated proteins, primary human preadipocytes were plated in 96-well dishes in the presence of rosiglitazone (rosi), a PPAR-(c) agonist that promotes adipogenesis. The cells were then labeled for nuclei, lipid droplets, and proteins such as perilipin, protein kinase C (PKC), and hormone-sensitive lipase (HSL). The cells were imaged via automated digital microscopy and algorithms were developed to quantify lipid droplet (Lipid Droplet algorithm) and protein expression and colocalization (Colocalization algorithm). The algorithms, which were incorporated into Vala Science Inc's CyteSeer((R)) image cytometry program, quantified the rosi-induced increases in lipid droplet number, size, and intensity, and the expression of perilipin with exceptional consistency (Z' values of 0.54-0.71). Regarding colocalization with lipid droplets, Pearson's correlation coefficients of 0.38 (highly colocalized), 0.16 (moderate), and -0.0010 (random) were found for perilipin, PKC, and HSL, respectively. For hepatocytes (AML12, HuH-7, and primary cells), the algorithms also quantified the stimulatory and inhibitory effect of oleic acid and triacsin C on lipid droplets (Z's &gt; 0.50) and ADFP expression/colocalization. Oleic acid-induced lipid droplets in HeLa cells and macrophages (THP-1) were also well quantified. The results suggest that HCA techniques can be utilized to quantify lipid droplets and associated proteins in many cell models relevant to a variety of diseases.

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Quantification of Lipid Droplets and Associated Proteins in Cellular Models of Obesity via High-Content/High-Throughput Microscopy and Automated Image Analysis